{"id":"W4366747489","doi":"10.1145/3593294","title":"Data Provenance in Security and Privacy","year":2023,"lang":"en","type":"review","venue":"ACM Computing Surveys","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick; University of Saskatchewan","funders":"Mitacs","keywords":"Provenance; Computer science; Metadata; Context (archaeology); Variety (cybernetics); Data science; Internet privacy; Computer security; World Wide Web","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","scholarly_communication","open_science"],"consensus_categories":["metaresearch","open_science"],"category_scores_codex":[0.07944627,0.0004363902,0.001778249,0.0008603818,0.0002286624,0.00128623,0.01107169,0.0001695347,0.00001558365],"category_scores_gemma":[0.04759109,0.0003295119,0.000137665,0.004010101,0.0001423335,0.0003163313,0.02774114,0.000588713,0.0007136906],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006232085,"about_ca_system_score_gemma":0.0002399254,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002595875,"about_ca_topic_score_gemma":0.0002804304,"domain_scores_codex":[0.9885143,0.003982512,0.002082474,0.003216015,0.00153989,0.0006648236],"domain_scores_gemma":[0.973757,0.01465561,0.00102405,0.01028722,0.0001310626,0.0001450751],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[3.326533e-7,0.00002785499,0.0005144608,0.001098835,0.00002086532,0.00003393523,0.0001199028,0.000006188575,1.941529e-9,0.00008011082,0.0242476,0.9738499],"study_design_scores_gemma":[0.0001173132,0.00001060823,0.002197624,0.004632697,0.00003624104,0.0000107793,0.00004687126,0.007999968,5.890482e-9,0.004889874,0.9796807,0.0003773203],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0002580542,0.9929422,0.001936154,0.0001138309,0.002530636,0.0009197038,0.0006092965,0.000241329,0.0004487665],"genre_scores_gemma":[0.0003789353,0.9954188,0.002006026,0.00003177475,0.000300298,0.000007771443,0.0008266616,0.00005990073,0.0009698454],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9734726,"threshold_uncertainty_score":0.9999157,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6060179517797337,"score_gpt":0.5269220062819636,"score_spread":0.0790959454977701,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}